Tuesday, August 25, 2026

Dolly Parton


source: People

Dolly Parton, a light gone out at 80. 

Dolly Rebecca Parton, the singer, songwriter, actress, entrepreneur and philanthropist who rose from a one-room cabin in the mountains of East Tennessee to become one of the most recognizable and beloved Americans in the world, died Tuesday at 80.

She was born Jan. 19, 1946, in Locust Ridge, Tennessee, the fourth of 12 children in a family that lived in considerable poverty. 

Her father, Robert Lee Parton, worked as a sharecropper and construction worker; her mother, Avie Lee, kept house and raised the children.

The family sang together,and Parton began performing publicly while still a child. 

She never forgot where she came from, and, for someone who became extraordinarily wealthy, she never seemed embarrassed by it.

Instead, poverty became one of the great subjects of her art.

She became one of the most prolific and versatile American entertainers of her generation, moving comfortably among country, pop, gospel, film, television and musical theater. 

The Country Music Hall of Fame and Museum, which inducted her in 1999, describes her career as one that expanded from country songwriting and singing into pop stardom, acting, business and philanthropy. 

The towering blonde hair, rhinestone-studded clothes, elaborate makeup and exaggerated femininity could easily have reduced another performer to a caricature. Parton instead turned them into an instrument of self-definition. 

She understood the joke.

Parton became a movie star, built an entertainment empire around her name and helped establish Dollywood near her hometown in the Great Smoky Mountains. 

In 1995, she established
Dolly Parton's Imagination Library, initially providing free books to children in her home county. 

The program was inspired in part by her father’s inability to read and write. It eventually expanded throughout the United States and internationally, sending age-appropriate books free of charge to children from birth to age five. 

By 2025, the program had passed the extraordinary milestone of 300 million books distributed. 

She contributed to medical research, children's hospitals and disaster relief, including substantial assistance to victims of the 2016 wildfires in the Smoky Mountains. 

In 2020, she donated $1 million to Vanderbilt University, helping support research that contributed to development of the Moderna COVID-19 vaccine.

She did not seem particularly interested in lecturing people about what they should believe. She simply used her extraordinary resources to do things she thought were useful.

Parton was a deeply religious woman from the American South whose music emerged from country, bluegrass and gospel traditions. 

She was also a feminist icon, a champion of literacy, an advocate for working women and a cultural figure embraced by people whose politics could otherwise have very little in common. 

This was perhaps the central achievement of Dolly Parton's public persona. She could be simultaneously glamorous and homespun, enormously wealthy and proudly Appalachian, commercially calculating and genuinely generous, self-mocking and fiercely ambitious. 

Her career produced numerous honors and distinctions, including membership in the Country Music Hall of Fame and the Songwriters Hall of Fame, Grammy Awards, and recognition from institutions far beyond country music. The 
American Library Association awarded her honorary lifetime membership for her contribution to literacy and libraries. 

She wrote thousands of songs. She sold more than 100 million records. She became a movie star, a television personality, a businesswoman and the proprietor of a major theme park. She created an international literacy program that put millions of books into the hands of children.

After accumulating wealth and fame on a scale few people from her circumstances could have imagined, she spent much of the second half of her life giving pieces of that success away.

She was Dolly Parton.




Diffusion Models Can Create New Images Without Infringing on Training Sources

A study of language models, especially those designed to create images and video, might suggest that copyright infringements by diffusion-based models might be impossible to prove. 


The authors say “locating a part of the training data that can be held responsible for a generated sample, can become impossible if a model is trained on a sufficiently large corpus of data.”


In other words, a large training set is all that is needed to render copyright traceable to any single source impossible. 


The study methodology relies on omitting an attributed unit from the training set (one image or all images by the same creator), the induced counterfactual sample cannot be directly attributed to that same unit. 


source: Nature Communications


In the above example, a generated image created from a smaller data set is more attributable to a single training image.


A large data set, on the other hand, creates a new image that shows no trace of a single training image.  


Model / family

Main generation

Diffusion?

Basic architecture / approach

GPT-4 / GPT-4o / GPT-5 family

Text, multimodal

No*

Primarily autoregressive Transformer for language

Claude 3/4 family

Text, code, multimodal

No*

Autoregressive Transformer

Gemini 1–4 family

Text, multimodal

No*

Primarily autoregressive Transformer

Llama family

Text, code

No

Autoregressive Transformer

Mistral / Mixtral family

Text, code

No

Autoregressive Transformer, including MoE

DeepSeek family

Text, code, reasoning

No

Autoregressive Transformer/MoE

Gemini Diffusion

Text/code

Yes

Text diffusion Transformer; generates blocks of tokens and iteratively refines them (Google DeepMind)

DiffusionGemma

Text/code

Yes

Non-sequential diffusion Transformer based on Gemma research (Google DeepMind)

Stable Diffusion 1.x/2.x/XL

Images

Yes

Latent diffusion, historically U-Net-based

Stable Diffusion 3/3.5

Images

Yes

Diffusion Transformer / MM-DiT

FLUX.1 / FLUX family

Images

Yes

Diffusion/flow-based Transformer

DALL·E 2

Images

Yes

Diffusion

DALL·E 3

Images

Yes

Diffusion-based image generation

Imagen 2/3/4

Images

Yes

Diffusion-based image generation; Google explicitly describes Imagen as a diffusion model (Google DeepMind)

Midjourney

Images

Yes*

Widely understood to use diffusion/related denoising techniques, though Midjourney does not disclose its architecture in detail

Adobe Firefly

Images/video

Yes*

Diffusion-based generation among its models; architecture varies by model

Sora

Video

Yes

Diffusion Transformer (DiT); starts from noisy video patches and denoises them (OpenAI)

Sora 2

Video

Yes*

Diffusion-based video generation; details of current implementation are less fully disclosed

Veo 2/3/3.1

Video

Yes*

Diffusion/Transformer-based video generation; Google describes Veo as drawing on its diffusion-model research (Google DeepMind)

Runway Gen-2/3

Video

Yes*

Diffusion-based video generation

Kling

Video

Yes*

Diffusion/Transformer-based video generation

Luma Dream Machine

Video

Yes*

Diffusion/Transformer-based video generation

MusicGen

Music/audio

No

Autoregressive Transformer over discrete audio tokens

AudioCraft / related models

Audio/music

Mixed

Different models use different architectures

Genie 2

Interactive worlds/video

Yes

Autoregressive latent diffusion—an interesting hybrid (Google DeepMind)


“Our findings provide a compelling case that attribution, the task of locating a unit of data within the training set that can be held responsible for a generated sample, is practically impossible on contemporary generative diffusion models,” say authors Zheng Dai and David Gifford.


Chatbot Usage Limits Now are Effectively Unlimited for Most Models for Light Users

Generative artificial intelligence model usage allowances for users on free plans have changed substantially since the first generation of each major model, generally following the pattern for internet access services: moving from usage limits to effectively unlimited for light users. 


ChatGPT might not have had formal usage limits, but access was the real constraint: the model was at capacity so often that many users found they could ask a few questions before hitting an effective block. 


Platform & Model at Launch

Initial Free Tier Launch Date

Initial Usage Limits (When First Introduced)

Reset Window / Conditions

OpenAI ChatGPT (Original GPT-3.5)

November 2022

No hard rigid prompt caps initially, but subject to broad error messages ("ChatGPT is at capacity right now") when servers were overloaded. Users could typically send dozens to hundreds of messages freely.

Dynamic system load throttling; no rolling time window concept at day one, just global traffic blocks.

Anthropic Claude (Original Claude 1)

March 2023

Roughly 50 to 100 messages per day depending on server traffic, as Anthropic quietly tested its early constitutional AI assistant against a smaller user base.

Reset daily at midnight.

Google Gemini (Originally launched as Bard using PaLM 2)

March 2023

No explicit hard numerical cap on prompt quantity for the web app interface during its initial experimental rollout, though safety filters and length restrictions applied.

Controlled dynamically by global capacity limits rather than a rigid per-hour user meter.

Microsoft Copilot (Originally launched as Bing Chat using GPT-4)

February 2023

Initially capped strictly at 5 turns per conversation and 50 queries per day to prevent erratic behavior and control high compute costs of early GPT-4. (Limits were quickly relaxed to 20/300 after initial tests).

Reset daily; individual chat sessions had to be wiped clean after hitting the turn limit.


Today, as more capacity has been added, some operations, such as text queries, are unlimited in principle on ChatGPT free plans, for example. 


Lighter users might seldom, if ever, encounter access blocking because servers are at capacity. 



Platform

Model Access (Free Tier)

Message / Usage Limits

Reset Window / Conditions

OpenAI ChatGPT

GPT-5.6 Luna (or equivalent lightweight default model)

Unlimited text chats (introduced for text as of August 2026); limits apply to heavy features like file/image uploads, voice mode, and image generation.

Weekly rolling limits or dynamic caps apply to resource-heavy features (like advanced tool usage or deep searches).

Anthropic Claude

Sonnet-class model (e.g., Sonnet 4.5)

Roughly 15 to 40 messages per rolling window. Limits are token-dependent (long documents or code pastes consume the quota much faster).

Rolling 5-hour window (refills continuously 5 hours after your first message; no midnight reset).

Google Gemini

Gemini Flash / Flash-Lite variants

Generous message allowances for general prompting; API free tier allows 5 to 15 Requests Per Minute (RPM) and up to 1,000 Requests Per Day (RPD) depending on the specific model.

Standard rolling limits apply on the consumer web app; API limits reset daily/continuously.

Microsoft Copilot

GPT-4o / latest integrated OpenAI flagship infrastructure

Standard daily chat caps (typically ranging around 30 to 50 turns per conversation, with a total daily cap around 300 messages depending on demand).

Resets daily or after starting a fresh conversation session.

Dolly Parton

source: People Dolly Parton, a light gone out at 80.  Dolly Rebecca Parton, the singer, songwriter, actress, entrepreneur and philanthro...